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- Ryan W. Gardner
Johns Hopkins University, Applied Physics Laboratory, Baltimore, MD, USA
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Publication search results
found 44 matches
- 2024
- Ryan W. Gardner, Tadayoshi Kohno, Alec Yasinsac:
Attacking the Diebold Signature Variant - RSA Signatures with Unverified High-order Padding. CoRR abs/2403.01048 (2024) - Noah Ford, Ryan W. Gardner, Austin Juhl, Nathan Larson:
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL). CoRR abs/2403.01059 (2024) - Lincoln Bryant, Robert W. Gardner, Fengping Hu, David Jordan, Ryan P. Taylor:
Kubernetes Deployment Options for On-Prem Clusters. CoRR abs/2407.01620 (2024) - 2023
- Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens, Raman Arora, I-Jeng Wang:
A Risk-Sensitive Approach to Policy Optimization. AAAI 2023: 15019-15027 - Ryan Gardner, Joshua Wilkins, Sejal Mistry, Ramkiran Gouripeddi, Julio C. Facelli:
Structural Homology of Epitope Binding Mimicry in the Onset of Type 1 Diabetes Mellitus. BIBM 2023: 3708-3713 - Ian Stewart Joyce, Grant Erdmann, Kirk P. Gardner, Ryan Kramer, Kyle Siegrist:
Topological Hierarchical Decompositions. CoRR abs/2312.10239 (2023) - 2022
- Ryan LaRose, Andrea Mari, Sarah Kaiser, Peter J. Karalekas, Andre A. Alves, Piotr Czarnik, Mohamed El Mandouh, Max Hunter Gordon, Yousef Hindy, Aaron Robertson, Purva Thakre, Misty Wahl, Danny Samuel, Rahul Mistri, Maxime Tremblay, Nick Gardner, Nathaniel T. Stemen, Nathan Shammah, William J. Zeng:
Mitiq: A software package for error mitigation on noisy quantum computers. Quantum 6: 774 (2022) - Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens, Raman Arora, I-Jeng Wang:
A Risk-Sensitive Approach to Policy Optimization. CoRR abs/2208.09106 (2022) - 2021
- Ryan W. Gardner, Gino Perrotta, Anvay Shah, Shivaram Kalyanakrishnan, Kevin A. Wang, Gregory Clark, Timo Bertram, Johannes Fürnkranz, Martin Müller, Brady P. Garrison, Prithviraj Dasgupta, Saeid Rezaei:
The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. NeurIPS (Competition and Demos) 2021: 119-132 - Gino Perrotta, Ryan W. Gardner, Corey Lowman, Mohammad Taufeeque, Nitish Tongia, Shivaram Kalyanakrishnan, Gregory Clark, Kevin Wang, Eitan Rothberg, Brady P. Garrison, Prithviraj Dasgupta, Callum Canavan, Lucas McCabe:
The Second NeurIPS Tournament of Reconnaissance Blind Chess. NeurIPS (Competition and Demos) 2021: 53-65 - 2020
- Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan W. Gardner, Daniel Genin, Joshua Silbermann, Michael P. Owen, Mykel J. Kochenderfer:
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning. J. Artif. Intell. Res. 69: 1165-1201 (2020) - 2019
- Ryan R. Curtin, Javier R. Echauz, Andrew B. Gardner:
Exploiting the structure of furthest neighbor search for fast approximate results. Inf. Syst. 80: 124-135 (2019) - Ryan R. Curtin, Andrew B. Gardner, Slawomir Grzonkowski, Alexey Kleymenov, Alejandro Mosquera:
Detecting DGA domains with recurrent neural networks and side information. ARES 2019: 20:1-20:10 - Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks:
Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. EDM 2019 - Josh Gardner, Christopher Brooks, Ryan Baker:
Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. LAK 2019: 225-234 - David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. NeurIPS 2019: 5497-5508 - Ryan W. Gardner, Corey Lowman, Casey Richardson, Ashley J. Llorens, Jared Markowitz, Nathan Drenkow, Andrew Newman, Gregory Clark, Gino Perrotta, Robert Perrotta, Timothy Highley, Vlad Shcherbina, William Bernadoni, Mark Jordan, Asen Asenov:
The First International Competition in Machine Reconnaissance Blind Chess. NeurIPS (Competition and Demos) 2019: 121-130 - David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. CoRR abs/1910.01739 (2019) - 2018
- Daniel M. Johnson, M. John Gardner, Ryan Perry:
Validation of two game experience scales: The Player Experience of Need Satisfaction (PENS) and Game Experience Questionnaire (GEQ). Int. J. Hum. Comput. Stud. 118: 38-46 (2018) - Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker:
MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data. IEEE BigData 2018: 3235-3244 - Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan Baker:
Replicating MOOC predictive models at scale. L@S 2018: 1:1-1:10 - Joe Breen, Lincoln Bryant, Gabriele Carcassi, Jiahui Chen, Robert W. Gardner, Ryan Harden, Martin Izdimirski, Robert Killen, Ben Kulbertis, Shawn McKee, Benedikt Riedel, Jason Stidd, Luan Truong, Ilija Vukotic:
Building the SLATE Platform. PEARC 2018: 5:1-5:7 - Josh Gardner, Christopher Brooks, Juan Miguel L. Andres, Ryan S. Baker:
MORF: A Framework for MOOC Predictive Modeling and Replication At Scale. CoRR abs/1801.05236 (2018) - Josh Gardner, Yuming Yang, Ryan S. Baker, Christopher Brooks:
Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining. CoRR abs/1806.05208 (2018) - Ryan R. Curtin, Andrew B. Gardner, Slawomir Grzonkowski, Alexey Kleymenov, Alejandro Mosquera:
Detecting DGA domains with recurrent neural networks and side information. CoRR abs/1810.02023 (2018) - Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan W. Gardner, Daniel Genin, Joshua Silbermann, Michael P. Owen, Mykel J. Kochenderfer:
Adaptive Stress Testing: Finding Failure Events with Reinforcement Learning. CoRR abs/1811.02188 (2018) - Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens:
On the Complexity of Reconnaissance Blind Chess. CoRR abs/1811.03119 (2018) - 2017
- Jean-Baptiste Jeannin, Khalil Ghorbal, Yanni Kouskoulas, Aurora C. Schmidt, Ryan W. Gardner, Stefan Mitsch, André Platzer:
A formally verified hybrid system for safe advisories in the next-generation airborne collision avoidance system. Int. J. Softw. Tools Technol. Transf. 19(6): 717-741 (2017) - Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, Andrew B. Gardner:
Detecting Adversarial Samples from Artifacts. CoRR abs/1703.00410 (2017) - 2016
- Ryan R. Curtin, Andrew B. Gardner:
Fast Approximate Furthest Neighbors with Data-Dependent Candidate Selection. SISAP 2016: 221-235
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